Amazon EMR (Elastic MapReduce) vs. Apache Pig

We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.

Incentivized

Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.

Incentivized

• The cluster size of MapReduce is very dynamic and therefore scalability is good for EMR.
• It also works well with other Amazon Web Services like Amazon Simple Storage Service, which means that data can be taken from those services and written back to them.
• I tried using the in-house hosting at the university I work in, but there would be a lot of complications with technical support required. For Amazon, the support and documentation was good to solve these problems faster.

Incentivized

• Iterative Development - you can write aliases/variables, which are not immediately executed and these are stored in a DAG, which is only evaluated upon dumping or storing another alias.
• Fast execution - Works with MapReduce, Tez, or Spark execution frameworks to provide fast run times at large scales.
• Local and remote interoperability - Scripts that depend on testing a small dataset locally before moving to the full thing can simply be done with "pig -x local."

Incentivized

• Sometimes bootstrapping certain tools comes with debugging costs. The tools provided by some of the enterprise editions are great compared to EMR.
• Like some of the enterprise editions EMR does not provide on premises options.
• No UI client for saving the workbooks or code snippets. Everything has to go through submitting process. Not really convenient for tracking the job as well.

Incentivized

• May not fit every need and a SQL-like abstraction may be more effective for some tasks (look at Spark-SQL, Hive, or even an actual DBMS)
• All Pig jobs are written in a Domain Specific Language so not a lot of transferable knowledge
• Writing your own User Defined Functions (UDFS) is a nice feature but can be painful to implement in practice

Incentivized

Documentation is quite good and the product is regularly updated, so new features regularly come out. The setup is straightforward enough, especially once you have already established the overall platform infrastructure and the aws-cli APIs are easy enough to use. It would be nice to have some out-of-the-box integrations for checking logs and the Spark UI, rather than relying on know-how and digging through multiple levels to find the informations

Incentivized

It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.

Incentivized

I give the overall support for Amazon EMR this rating because while the support technicians are very knowledgeable and always able to help, it sometimes takes a very long time to get in contact with one of the support technicians. So overall the support is pretty good for Amazon EMR.

Incentivized

The documentation is adequate. I'm not sure how large of an external community there is for support.

Incentivized

Snowflake is a lot easier to get started with than the other options. Snowflake's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.

Incentivized

It takes me less time to write a Pig script than get a Spark program running for batch ETL workloads. Compared to Spark, Pig has a steeper learning curve because it employs a proprietary programming language. In one script and one fine, it can handle both Map Reduce and Hadoop. It has a large amount of documentation available to make learning more convenient.

Incentivized

• It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
• It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
• It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.

Incentivized

• Return on Investments are significant considering what it can do with traditional analysis techniques. But, other alternatives like Apache Spark, Hive being more efficient, it is hard to stick to Apache Pig.
• It can handle large datasets pretty easily compared to SQL. But, again, alternatives are more efficient.
• While working on unstructured, decentralized dataset, Pig is highly beneficial, as it is not a complete deviation from SQL, but it does not take you in complexity MapReduce as well.

Incentivized